Creates a new Microphone instance, which represents a physical microphone on the computer. Subclass of AudioSource.
This will throw an AttributeError if you don't have PyAudio 0.2.9 or later installed.
If device_index is unspecified or None, the default microphone is used as the audio source. Otherwise, device_index should be the index of the device to use for audio input.
A device index is an integer between 0 and pyaudio.get_device_count() - 1 (assume we have used import pyaudio beforehand) inclusive. It represents an audio device such as a microphone or speaker. See the PyAudio documentation for more details.
The microphone audio is recorded in chunks of chunk_size samples, at a rate of sample_rate samples per second (Hertz).
Higher sample_rate values result in better audio quality, but also more bandwidth (and therefore, slower recognition). Additionally, some machines, such as some Raspberry Pi models, can't keep up if this value is too high.
Higher chunk_size values help avoid triggering on rapidly changing ambient noise, but also makes detection less sensitive. This value, generally, should be left at its default.
Instances of this class are context managers, and are designed to be used with with statements:
with Microphone() as source: # open the microphone and start recording
pass # do things here - ``source`` is the Microphone instance created above
# the microphone is automatically released at this pointReturns a list of the names of all available microphones. For microphones where the name can't be retrieved, the list entry contains None instead.
The index of each microphone's name is the same as its device index when creating a Microphone instance - indices in this list can be used as values of device_index.
To create a Microphone instance by name:
m = None
for microphone_name in Microphone.list_microphone_names():
if microphone_name == "HDA Intel HDMI: 0 (hw:0,3)":
m = Microphone(i)Creates a new WavFile instance given a WAV audio file filename_or_fileobject. Subclass of AudioSource.
If filename_or_fileobject is a string, then it is interpreted as a path to a WAV audio file (mono or stereo) on the filesystem. Otherwise, filename_or_fileobject should be a file-like object such as io.BytesIO or similar.
Note that using functions that read from the audio (such as recognizer_instance.record or recognizer_instance.listen) will move ahead in the stream. For example, if you execute recognizer_instance.record(wavfile_instance, duration=10) twice, the first time it will return the first 10 seconds of audio, and the second time it will return the 10 seconds of audio right after that.
Note that the WAV file must be in PCM/LPCM format; WAVE_FORMAT_EXTENSIBLE and compressed WAV are not supported and may result in undefined behaviour.
Instances of this class are context managers, and are designed to be used with with statements:
import speech_recognition as sr
with sr.WavFile("SOMETHING.wav") as source: # open the WAV file for reading
pass # do things here - ``source`` is the WavFile instance created aboveRepresents the length of the audio stored in the WAV file in seconds. This property is only available when inside a context - essentially, that means it should only be accessed inside a with wavfile_instance ... statement. Outside of contexts, this property is None.
This is useful when combined with the offset parameter of recognizer_instance.record, since when together it is possible to perform speech recognition in chunks.
However, note that recognizing speech in multiple chunks is not the same as recognizing the whole thing at once. If spoken words appear on the boundaries that we split the audio into chunks on, each chunk only gets part of the word, which may result in inaccurate results.
Creates a new Recognizer instance, which represents a collection of speech recognition settings and functionality.
Represents the energy level threshold for sounds. Values below this threshold are considered silence, and values above this threshold are considered speech. Can be changed.
This is adjusted automatically if dynamic thresholds are enabled (see recognizer_instance.dynamic_energy_threshold). A good starting value will generally allow the automatic adjustment to reach a good value faster.
This threshold is associated with the perceived loudness of the sound, but it is a nonlinear relationship. The actual energy threshold you will need depends on your microphone sensitivity or audio data. Typical values for a silent room are 0 to 100, and typical values for speaking are between 150 and 3500. Ambient (non-speaking) noise has a significant impact on what values will work best.
If you're having trouble with the recognizer trying to recognize words even when you're not speaking, try tweaking this to a higher value. If you're having trouble with the recognizer not recognizing your words when you are speaking, try tweaking this to a lower value. For example, a sensitive microphone or microphones in louder rooms might have a ambient energy level of up to 4000:
import speech_recognition as sr
r = sr.Recognizer()
r.energy_threshold = 4000
# rest of your code goes hereThe dynamic energy threshold setting can mitigate this by increasing or decreasing this automatically to account for ambient noise. However, this takes time to adjust, so it is still possible to get the false positive detections before the threshold settles into a good value.
To avoid this, use recognizer_instance.adjust_for_ambient_noise(source, duration = 1) to calibrate the level to a good value. Alternatively, simply set this property to a high value initially (4000 works well), so the threshold is always above ambient noise levels: over time, it will be automatically decreased to account for ambient noise levels.
Represents whether the energy level threshold (see recognizer_instance.energy_threshold) for sounds should be automatically adjusted based on the currently ambient noise level while listening. Can be changed.
Recommended for situations where the ambient noise level is unpredictable, which seems to be the majority of use cases. If the ambient noise level is strictly controlled, better results might be achieved by setting this to False to turn it off.
If the dynamic energy threshold setting is enabled (see recognizer_instance.dynamic_energy_threshold), represents approximately the fraction of the current energy threshold that is retained after one second of dynamic threshold adjustment. Can be changed (not recommended).
Lower values allow for faster adjustment, but also make it more likely to miss certain phrases (especially those with slowly changing volume). This value should be between 0 and 1. As this value approaches 1, dynamic adjustment has less of an effect over time. When this value is 1, dynamic adjustment has no effect.
If the dynamic energy threshold setting is enabled (see recognizer_instance.dynamic_energy_threshold), represents the minimum factor by which speech is louder than ambient noise. Can be changed (not recommended).
For example, the default value of 1.5 means that speech is at least 1.5 times louder than ambient noise. Smaller values result in more false positives (but fewer false negatives) when ambient noise is loud compared to speech.
Represents the minimum length of silence (in seconds) that will register as the end of a phrase. Can be changed.
Smaller values result in the recognition completing more quickly, but might result in slower speakers being cut off.
Records up to duration seconds of audio from source (an AudioSource instance) starting at offset (or at the beginning if not specified) into an AudioData instance, which it returns.
If duration is not specified, then it will record until there is no more audio input.
Adjusts the energy threshold dynamically using audio from source (an AudioSource instance) to account for ambient noise.
Intended to calibrate the energy threshold with the ambient energy level. Should be used on periods of audio without speech - will stop early if any speech is detected.
The duration parameter is the maximum number of seconds that it will dynamically adjust the threshold for before returning. This value should be at least 0.5 in order to get a representative sample of the ambient noise.
Records a single phrase from source (an AudioSource instance) into an AudioData instance, which it returns.
This is done by waiting until the audio has an energy above recognizer_instance.energy_threshold (the user has started speaking), and then recording until it encounters recognizer_instance.pause_threshold seconds of non-speaking or there is no more audio input. The ending silence is not included.
The timeout parameter is the maximum number of seconds that it will wait for a phrase to start before giving up and throwing an speech_recognition.WaitTimeoutError exception. If timeout is None, it will wait indefinitely.
Spawns a thread to repeatedly record phrases from source (an AudioSource instance) into an AudioData instance and call callback with that AudioData instance as soon as each phrase are detected.
Returns a function object that, when called, requests that the background listener thread stop, and waits until it does before returning. The background thread is a daemon and will not stop the program from exiting if there are no other non-daemon threads.
Phrase recognition uses the exact same mechanism as recognizer_instance.listen(source).
The callback parameter is a function that should accept two parameters - the recognizer_instance, and an AudioData instance representing the captured audio. Note that callback function will be called from a non-main thread.
Performs speech recognition on audio_data (an AudioData instance), using CMU Sphinx.
The recognition language is determined by language, an IETF language tag like "en-US" or "en-GB", defaulting to US English. Out of the box, only en-US is supported. See Notes on using `PocketSphinx for information about installing other languages. This document is also included under reference/pocketsphinx.rst.
Returns the most likely transcription if show_all is false (the default). Otherwise, returns the Sphinx pocketsphinx.pocketsphinx.Hypothesis object generated by Sphinx.
Raises a speech_recognition.UnknownValueError exception if the speech is unintelligible. Raises a speech_recognition.RequestError exception if there are any issues with the Sphinx installation.
Performs speech recognition on audio_data (an AudioData instance), using the Google Speech Recognition API.
The Google Speech Recognition API key is specified by key. If not specified, it uses a generic key that works out of the box. This should generally be used for personal or testing purposes only, as it may be revoked by Google at any time.
To obtain your own API key, simply follow the steps on the API Keys page at the Chromium Developers site. In the Google Developers Console, Google Speech Recognition is listed as "Speech API". Note that the API quota for your own keys is 50 requests per day, and there is currently no way to raise this limit.
The recognition language is determined by language, an IETF language tag like "en-US" or "en-GB", defaulting to US English. A list of supported language codes can be found here. Basically, language codes can be just the language (en), or a language with a dialect (en-US).
Returns the most likely transcription if show_all is false (the default). Otherwise, returns the raw API response as a JSON dictionary.
Raises a speech_recognition.UnknownValueError exception if the speech is unintelligible. Raises a speech_recognition.RequestError exception if the key isn't valid, the quota for the key is maxed out, or there is no internet connection.
Performs speech recognition on audio_data (an AudioData instance), using the Wit.ai API.
The Wit.ai API key is specified by key. Unfortunately, these are not available without signing up for an account and creating an app. You will need to add at least one intent (recognizable sentence) before the API key can be accessed, though the actual intent values don't matter.
To get the API key for a Wit.ai app, go to the app settings, go to the section titled "API Details", and look for "Server Access Token" or "Client Access Token". If the desired field is blank, click on the "Reset token" button on the right of the field. Wit.ai API keys are 32-character uppercase alphanumeric strings.
Though Wit.ai is designed to be used with a fixed set of phrases, it still provides services for general-purpose speech recognition.
The recognition language is configured in the Wit.ai app settings.
Returns the most likely transcription if show_all is false (the default). Otherwise, returns the raw API response as a JSON dictionary.
Raises a speech_recognition.UnknownValueError exception if the speech is unintelligible. Raises a speech_recognition.RequestError exception if the key isn't valid, the quota for the key is maxed out, or there is no internet connection.
recognizer_instance.recognize_ibm(audio_data, username, password, language = "en-US", show_all = False)
Performs speech recognition on audio_data (an AudioData instance), using the IBM Speech to Text API.
The IBM Speech to Text username and password are specified by username and password, respectively. Unfortunately, these are not available without an account. IBM has published instructions for obtaining these credentials in the IBM Watson Developer Cloud documentation.
The recognition language is determined by language, an IETF language tag with a dialect like "en-US" or "es-ES", defaulting to US English. At the moment, this supports the tags "en-US" and "es-ES".
Returns the most likely transcription if show_all is false (the default). Otherwise, returns the raw API response as a JSON dictionary.
Raises a speech_recognition.UnknownValueError exception if the speech is unintelligible. Raises a speech_recognition.RequestError exception if an error occurred, such as an invalid key, or a broken internet connection.
recognizer_instance.recognize_att(audio_data, app_key, app_secret, language = "en-US", show_all = False)
Performs speech recognition on audio_data (an AudioData instance), using the AT&T Speech to Text API.
The AT&T Speech to Text app key and app secret are specified by app_key and app_secret, respectively. Unfortunately, these are not available without signing up for an account and creating an app.
To get the app key and app secret for an AT&T app, go to the My Apps page and look for "APP KEY" and "APP SECRET". AT&T app keys and app secrets are 32-character lowercase alphanumeric strings.
The recognition language is determined by language, an IETF language tag with a dialect like "en-US" or "es-ES", defaulting to US English. At the moment, this supports the tags "en-US" and "es-ES".
Returns the most likely transcription if show_all is false (the default). Otherwise, returns the raw API response as a JSON dictionary.
Raises a speech_recognition.UnknownValueError exception if the speech is unintelligible. Raises a speech_recognition.RequestError exception if the key isn't valid, or there is no internet connection.
Base class representing audio sources. Do not instantiate.
Instances of subclasses of this class, such as Microphone and WavFile, can be passed to things like recognizer_instance.record and recognizer_instance.listen.
Storage class for audio data. Do not instantiate.
Instances of this class are returned from recognizer_instance.record and recognizer_instance.listen, and are passed to callbacks of recognizer_instance.listen_in_background.
Returns a byte string representing the raw frame data for the audio represented by the AudioData instance.
If convert_rate is specified and the audio sample rate is not convert_rate Hz, the resulting audio is resampled to match.
If convert_width is specified and the audio samples are not convert_width bytes each, the resulting audio is converted to match.
Writing these bytes directly to a file results in a valid RAW/PCM audio file.
Returns a byte string representing the contents of a WAV file containing the audio represented by the AudioData instance.
If convert_width is specified and the audio samples are not convert_width bytes each, the resulting audio is converted to match.
If convert_rate is specified and the audio sample rate is not convert_rate Hz, the resulting audio is resampled to match.
Writing these bytes directly to a file results in a valid WAV file.
Returns a byte string representing the contents of a FLAC file containing the audio represented by the AudioData instance.
If convert_rate is specified and the audio sample rate is not convert_rate Hz, the resulting audio is resampled to match.
If convert_width is specified and the audio samples are not convert_width bytes each, the resulting audio is converted to match.
Writing these bytes directly to a file results in a valid FLAC file.